Sn‐Doped Cs3Cu2I5 Microcrystals with High Photoluminescence and Hydrochromic Stability for Anti‐Counterfeiting and Encryption Applications

Cu‐based perovskites have excellent optical properties, environmental friendliness, and broad optical applications. However, their development is hindered by complex preparation methods and poor hydrochromic stability. In this work, a solvent‐evaporation‐based crystallization strategy combined with Sn doping for the preparation of Cs3Cu2I5 microcrystals (MCs) is reported. This strategy enhances the photoluminescence quantum yield (PLQY) and hydrochromic reliability. In addition, first‐principles calculations reveal the effect of Sn doping on the lattice structure and exciton–phonon coupling of the Cs3Cu2I5 MCs. The results show that Sn doping reduces the lattice spacing as a result of the substitution radius difference between Cu+ and Sn2+ ions, which causes stronger exciton–phonon coupling and enhances the PLQY. Moreover, the Sn‐doped Cs3Cu2I5 MCs show excellent reliability on heating and under hydrochromic cycles and long‐term luminescence conditions. Based on their hydrochromic and temperature‐responsive properties, the Sn‐doped Cs3Cu2I5 MCs are applied as encryptable printing materials for information encryption and decryption and as a fluorescence‐based temperature sensor, combined with Machine‐Learning to guide the manufacturing of fluorescent thermometers. The findings provide insights into the commercial value of copper‐based perovskites and demonstrate their potential anti‐counterfeiting, fluorescence temperature sensing applications. The synthesis of Sn‐doped Cs3Cu2I5 microcrystals (MCs) with high photoluminescence quantum yield and hydrochromic stability by solvent‐evaporation‐induced crystallization is reported. First‐principles calculations reveal that Sn doping induces lattice contraction and enhances exciton–phonon coupling. The Sn‐doped Cs3Cu2I5 MCs are applied as printing materials for anti‐counterfeiting applications and combine fluorescence‐based temperature sensing with machine‐learning to guide the production of fluorescent thermometers.